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A human watching a video of closely-packed cells can generally identify everyindividual cell, regardless of density and noise, but most currently-availablecell-tracking software cannot. This is because the human brain automaticallybuilds a physical model of the scene as it progresses, allowing it to readilydistinguish cells from noise and not be unduly confused by overlapping cells.Here we introduce software that uses physical rules to create a simulation ofthe activity in a cell video, synchronizing itself with the video as theactivity progresses. Because our simulation includes every individual cell, weare trivially able to track all cell movement, growth, and divisions. Ourmethod is also particularly robust to noise without requiring any substantialimage processing. We demonstrate the effectiveness of this method by trackingthe motion and lineage tree of a densely-packed colony of cells that grows from4 to more than 200 individuals.

No cell left behind: automated physics-based tracking of {\em every} cell in a dense and growing colony